A decoder for a receiver in a communication system includes an interface configured to receive encoded input data via a communication channel. The encoded input data includes forward error correction (FEC) codewords. A processor is configured to decode the FEC codewords using low density parity check (LDPC) codes defined by a parity check matrix. The parity check matrix is defined by both regular column partition (RCP) constraints and quasi-cyclic (QC) constraints. An output circuit is configured to output a decoded codeword based on the FEC codewords decoded by the processor.

A polar decoder kernal is described. The polar decoder kernal is configured to: receive one or more soft bits from a soft kernal encoded block having a block size of N and output one or more recovered kernal information bits from a recovered kernal information block having a block size of N. The polar decoder kernal comprises a decomposition of a polar code graph into an arbitrary number of columns depending on the kernal block size N.

A method of encoding input data. The method includes receiving a plurality of data bits of a bit stream. The method further includes forming words using the plurality of data bits to create a plurality of data packets including a first data packet. The method further includes encoding the words of the first data packet into coded words, partitioning the coded words into a plurality of blocks of M words each and integrating the coded words in each block in an interleaved order to generate a coded data packet for transmission through a communication channel.

A data storage device includes a memory device and a controller coupled to the memory device. The controller is configured to determine an error correction code (ECC) code length for KV pair data and/or an ECC code rate for the KV pair data, where the ECC code length and the ECC code rate are selected according to a value length and decoding capability of the KV pair data, generate ECC parity based on the selecting, and program the KV pair data and the generated ECC parity to the memory device.

Read data associated with Flash storage that is in a Flash storage state is received. One of a plurality of log-likelihood ratio (LLR) mapping tables is selected based at least in part on: (1) the Flash storage state and (2) a decoding attempt count associated with a finite-precision low-density parity-check (LDPC) decoder. A set of one or more LLR values is generated using the read data and the selected LLR mapping table, where each LLR value in the set of LLR values has a same finite precision as the finite-precision LDPC decoder. The finite-precision LDPC decoder generates the error-corrected read data using the set of LLR values and outputs it.

Techniques related to improving power consumption of an LDPC decoder are described. In an example, the LDPC decoder uses a message passing algorithm between variable nodes and check nodes. A check node processing unit that generates check node to variable node messages implements a plurality of check node processing mode. Operation in each mode consumes a certain amount of power while providing a certain accuracy. Depending on a reliability of a variable node to check node message received by the check node processing unit, an appropriate check node processing mode is selected and used to generate a corresponding check node to variable node message. The reliability can be estimated for a set of variable node to check node messages based on, for instance, syndrome-related parameters.

This invention presents a method and apparatus for vertical layered finite alphabet iterative decoding of low-density parity-check codes (LDPC) which operate on parity check matrices that consist of blocks of sub-matrices. The iterative decoding involves passing messages between variable nodes and check nodes of the Tanner graph that associated with one or more sub-matrices constitute decoding blocks, and the messages belong to a finite alphabet. Various embodiments for the method and apparatus of the invention are presented that can achieve very high throughputs with low hardware resource usage and power.

A memory includes, in one embodiment, NAND elements; read/write circuitry; and compressed soft-bit circuitry. The compressed soft-bit circuitry is configured to determine or receive one or more NAND conditions and then determine a soft-bit delta and select a compression scheme based on the NAND conditions. The read/write circuitry is configured to read a set of hard bits from the NAND elements and sense a first set of soft-bits using the determined soft-bit delta while reading the set of hard bits from the NAND elements. The first set of soft-bits has a first fixed size, and each soft-bit of the first set of soft-bits indicates a reliability of a corresponding hard bit of the set of hard bits. The compressed soft-bit circuitry is also configured to generate a second set of soft-bits based on the selected compression scheme and output the second set of soft-bits to a controller.

Provided herein may be an electronic device using an artificial neural network. The electronic device may include a training data generator configured to determine an input vector corresponding to a trapping set, detected during error correction decoding corresponding to a codeword, and a target vector corresponding to the input vector, and a training component configured to train an artificial neural network based on supervised learning by inputting the input vector to an input layer of the artificial neural network and by inputting the target vector to an output layer of the artificial neural network.

A device is disclosed. The device may include an input buffer to receive a first low bit width message. A reconstruction circuit may implement a reconstruction function on the first low bit width message, producing a first high bit width message. A computation circuit may implementing a computation function on the first high bit width message, producing a second high bit width message. A quantization circuit may implementing a quantization function on the second high bit width message, producing a second low bit width message. A decision buffer may then store the second low bit width message. The reconstruction function and the quantization function may vary depending on an iteration and a layer of the device.